Machine learning models for predicting the risk of depressive symptoms in Chinese college students
IntroductionDepression is highly prevalent among college students, and accurately identifying risk factors is essential for timely intervention. Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1648585/full |
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| author | Chengfu Yu Xiangxuan Kong Weijie Yu Xingcan Ni Jing Chen Xiaoyan Liao |
| author_facet | Chengfu Yu Xiangxuan Kong Weijie Yu Xingcan Ni Jing Chen Xiaoyan Liao |
| author_sort | Chengfu Yu |
| collection | DOAJ |
| description | IntroductionDepression is highly prevalent among college students, and accurately identifying risk factors is essential for timely intervention. Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. Four machine- learning algorithms, Random Forest, XGBoost, LightGBM, and Support Vector Machine, were evaluated.ResultsResults showed that the Random Forest model achieved the highest discriminant performance with an AUC of 0.87 and an accuracy of 0.79, and identified key predictors such as sleep disturbance, perceived stress, experiential avoidance, and self-criticism. SHapley Additive exPlanations analysis further revealed that deteriorating sleep quality and heightened stress levels significantly increased the risk of depressive symptoms.DiscussionThese findings validate the effectiveness of Random Forest in capturing complex data interactions and offer actionable insights for targeted mental health interventions. Future studies should improve generalizability by incorporating more diverse samples and physiological biomarkers. |
| format | Article |
| id | doaj-art-9e7b558ba5fa4119bf3289d59776d9bb |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-9e7b558ba5fa4119bf3289d59776d9bb2025-08-20T03:02:30ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-08-011610.3389/fpsyt.2025.16485851648585Machine learning models for predicting the risk of depressive symptoms in Chinese college studentsChengfu Yu0Xiangxuan Kong1Weijie Yu2Xingcan Ni3Jing Chen4Xiaoyan Liao5Department of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, ChinaDepartment of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, ChinaDepartment of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, ChinaDepartment of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, ChinaDepartment of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, ChinaSchool of Psychology, South China Normal University, Guangzhou, Guangdong, ChinaIntroductionDepression is highly prevalent among college students, and accurately identifying risk factors is essential for timely intervention. Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. Four machine- learning algorithms, Random Forest, XGBoost, LightGBM, and Support Vector Machine, were evaluated.ResultsResults showed that the Random Forest model achieved the highest discriminant performance with an AUC of 0.87 and an accuracy of 0.79, and identified key predictors such as sleep disturbance, perceived stress, experiential avoidance, and self-criticism. SHapley Additive exPlanations analysis further revealed that deteriorating sleep quality and heightened stress levels significantly increased the risk of depressive symptoms.DiscussionThese findings validate the effectiveness of Random Forest in capturing complex data interactions and offer actionable insights for targeted mental health interventions. Future studies should improve generalizability by incorporating more diverse samples and physiological biomarkers.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1648585/fullmachine learningdepressive symptomsrisk and protective factorscollege studentsrandom forest |
| spellingShingle | Chengfu Yu Xiangxuan Kong Weijie Yu Xingcan Ni Jing Chen Xiaoyan Liao Machine learning models for predicting the risk of depressive symptoms in Chinese college students Frontiers in Psychiatry machine learning depressive symptoms risk and protective factors college students random forest |
| title | Machine learning models for predicting the risk of depressive symptoms in Chinese college students |
| title_full | Machine learning models for predicting the risk of depressive symptoms in Chinese college students |
| title_fullStr | Machine learning models for predicting the risk of depressive symptoms in Chinese college students |
| title_full_unstemmed | Machine learning models for predicting the risk of depressive symptoms in Chinese college students |
| title_short | Machine learning models for predicting the risk of depressive symptoms in Chinese college students |
| title_sort | machine learning models for predicting the risk of depressive symptoms in chinese college students |
| topic | machine learning depressive symptoms risk and protective factors college students random forest |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1648585/full |
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